Random Forests with tidymodels
Learn how to train a random forest ensemble using tidymodels.
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Training a random forest
One advantage of the workflow approach of tidymodels is the ability to change the machine learning algorithm quickly. The following code compares using a CART classification tree vs. a random classification forest.
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#================================================================================================# Specify a CART classification tree#titanic_model <- decision_tree() %>%set_engine("rpart") %>%set_mode("classification")#================================================================================================# Specify a random classification forest#titanic_model <- rand_forest(trees = 500) %>%set_engine("randomForest") %>%set_mode("classification")
The code above uses the rand_forest() function from the parsnip package to specify using the random forest algorithm with default hyperparameter values. The code further specifies using the randomForest package’s algorithm to train a random ...